Objective:To evaluate the association between postoperative delirium (POD) and preoperative frailty in elderly patients undergoing knee or hip arthroplasty.Methods:This nested case-control study utilized medical records from elderly patients who underwent knee or hip arthroplasty under combined spinal-epidural anesthesia at Qingdao Municipal Hospital between September 2021 and May 2023. Participants were divided into 2 groups based on clinically diagnosed POD: POD group ( n=53) and non-POD group ( n=256). Univariate analysis was conducted on suspected influencing factors, and logistic regression analysis was utilized to identify the risk factors for POD. Receiver operating characteristic and clinical decision curves were plotted to evaluate the predictive performance of these risk factors for POD. Mediation analysis was performed, and a clinically applicable nomogram was constructed to achieve visual prediction of outcomes. Results:There were statistically significant differences in age, preoperative frailty, body mass index, American Society of Anesthesiologists Physical Status classification, Memorial Delirium Assessment Scale scores, and concentrations of Aβ 42, Aβ 40, phosphorylated tau protein (p-tau protein) and tau protein, Aβ 42/tau ratio and Aβ 42/p-tau ratio in cerebrospinal fluid (CSF) between non-POD group and POD group ( P<0.05). Preoperative frailty was a risk factor for POD ( P<0.05). Mediation analysis revealed that the association between preoperative frailty and POD was mediated by CSF tau protein concentrations. The area under the receiver operating characteristic curve of preoperative frailty and CSF biomarker concentrations in predicting POD was 0.974 ( P<0.05). The clinical decision curve demonstrated that the model combining the preoperative frailty and CSF biomarker concentrations predicted a higher net benefit ( P<0.05). The clinical decision curve showed that the model combining preoperative frailty and CSF biomarker concentrations predicted a higher net benefit. Conclusions:Preoperative frailty is a risk factor for POD in elderly patients undergoing knee or hip arthroplasty, and its combination with CSF biomarker concentrations can effectively predict the occurrence of POD. CSF tau concentration mediates the association between preoperative frailty and development of POD.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To explore the latent profiles of work withdrawal behaviors of junior nurses and their relationship with workplace social capital.Methods:Using the convenience sampling method, from July to August 2023, 348 junior nurses from five Class Ⅲ and seven ClassⅡ public hospitals in Ningxia Hui Autonomous Region were selected as the research objects. They were investigated with a General Information Questionnaire, the Work Withdrawal Behavior Scale, and the Workplace Social Capital Scale. Latent profile analysis was used to explore the categories of work withdrawal behaviors of junior nurses, and the differences in workplace social capital levels among junior nurses of different categories were compared.Results:A total of 348 questionnaires were recovered online in this survey, and 342 questionnaires were valid, with a valid rate of 98.3%. The work withdrawal behaviors of 342 junior nurses could be divided into three latent profiles, including 246 junior nurses (71.9%) in the low psychological-low behavioral withdrawal group, 81 junior nurses (23.7%) in the high psychological-low behavioral withdrawal group, and 15 junior nurses (4.4%) in the high psychological-high behavioral withdrawal group. The results of the unordered multinomial Logistic regression analysis showed that gender, whether they love nursing work or not, the average number of night shifts per month, the workplace social capital, and working years were the influencing factors of the work withdrawal behaviors of junior nurses ( P<0.05) . There were statistically significant differences in the workplace social capital among the three categories of junior nurses ( H=83.82, P<0.01) . Conclusions:There are three categories of work withdrawal behaviors among junior nurses, and there are differences in workplace social capital among junior nurses of different categories. Nursing managers should intervene and support junior nurses according to the characteristics of different categories to improve their workplace social capital levels.

Objective:To evaluate the association between postoperative delirium (POD) and preoperative frailty in elderly patients undergoing knee or hip arthroplasty.Methods:This nested case-control study utilized medical records from elderly patients who underwent knee or hip arthroplasty under combined spinal-epidural anesthesia at Qingdao Municipal Hospital between September 2021 and May 2023. Participants were divided into 2 groups based on clinically diagnosed POD: POD group ( n=53) and non-POD group ( n=256). Univariate analysis was conducted on suspected influencing factors, and logistic regression analysis was utilized to identify the risk factors for POD. Receiver operating characteristic and clinical decision curves were plotted to evaluate the predictive performance of these risk factors for POD. Mediation analysis was performed, and a clinically applicable nomogram was constructed to achieve visual prediction of outcomes. Results:There were statistically significant differences in age, preoperative frailty, body mass index, American Society of Anesthesiologists Physical Status classification, Memorial Delirium Assessment Scale scores, and concentrations of Aβ 42, Aβ 40, phosphorylated tau protein (p-tau protein) and tau protein, Aβ 42/tau ratio and Aβ 42/p-tau ratio in cerebrospinal fluid (CSF) between non-POD group and POD group ( P<0.05). Preoperative frailty was a risk factor for POD ( P<0.05). Mediation analysis revealed that the association between preoperative frailty and POD was mediated by CSF tau protein concentrations. The area under the receiver operating characteristic curve of preoperative frailty and CSF biomarker concentrations in predicting POD was 0.974 ( P<0.05). The clinical decision curve demonstrated that the model combining the preoperative frailty and CSF biomarker concentrations predicted a higher net benefit ( P<0.05). The clinical decision curve showed that the model combining preoperative frailty and CSF biomarker concentrations predicted a higher net benefit. Conclusions:Preoperative frailty is a risk factor for POD in elderly patients undergoing knee or hip arthroplasty, and its combination with CSF biomarker concentrations can effectively predict the occurrence of POD. CSF tau concentration mediates the association between preoperative frailty and development of POD.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To explore the latent profiles of work withdrawal behaviors of junior nurses and their relationship with workplace social capital.Methods:Using the convenience sampling method, from July to August 2023, 348 junior nurses from five Class Ⅲ and seven ClassⅡ public hospitals in Ningxia Hui Autonomous Region were selected as the research objects. They were investigated with a General Information Questionnaire, the Work Withdrawal Behavior Scale, and the Workplace Social Capital Scale. Latent profile analysis was used to explore the categories of work withdrawal behaviors of junior nurses, and the differences in workplace social capital levels among junior nurses of different categories were compared.Results:A total of 348 questionnaires were recovered online in this survey, and 342 questionnaires were valid, with a valid rate of 98.3%. The work withdrawal behaviors of 342 junior nurses could be divided into three latent profiles, including 246 junior nurses (71.9%) in the low psychological-low behavioral withdrawal group, 81 junior nurses (23.7%) in the high psychological-low behavioral withdrawal group, and 15 junior nurses (4.4%) in the high psychological-high behavioral withdrawal group. The results of the unordered multinomial Logistic regression analysis showed that gender, whether they love nursing work or not, the average number of night shifts per month, the workplace social capital, and working years were the influencing factors of the work withdrawal behaviors of junior nurses ( P<0.05) . There were statistically significant differences in the workplace social capital among the three categories of junior nurses ( H=83.82, P<0.01) . Conclusions:There are three categories of work withdrawal behaviors among junior nurses, and there are differences in workplace social capital among junior nurses of different categories. Nursing managers should intervene and support junior nurses according to the characteristics of different categories to improve their workplace social capital levels.

Objective:To explore the relationships among 4 primary indicators in the performance assessment and evaluation system of tertiary public hospitals.Methods:A questionnaire survey was employed to collect data on healthcare professionals'perceptions of the rationality of performance evaluation indicators.Structural equation modeling(SEM)and Amos 21.0 Statistical Software were utilized for data analysis,with the mediation effects tested using the Bootstrap method.Results:A total of 826 valid questionnaires were obtained.Medical quality,operational efficiency,and sustainable development had a positive impact on satisfaction.Operational efficiency and sustainable development played a chain-mediated role in the process of medical quality influencing satisfaction evaluation,namely"medical quality → operational efficiency → sustainable development → satisfaction".Conclusion:It helps to flexibly apply and deepen the integration of knowledge in the field of enterprise management into the field of hospital management,helps hospitals to scientifically formulate internal performance evaluation programs under the premise of limited resources,and provides new ideas for the country to optimize the performance appraisal index system of tertiary public hospitals.

Objective To investigate short-term clinical efficacy of autologous leukocyte-poor platelet-rich plasma(LP-PRP)treatment of knee osteoarthritis(KO A).Methods 85 cases of patients with Keligren Lawrence grade Ⅰ-Ⅲ knee os-teoarthritis in Peking University First Hospital Taiyuan Hospital(Taiyuan Central Hospital)from 2022 to 2023 were collect-ed for autologous LP-PRP collection and quality assessment using a blood component separator,and all patients were treated with autologous LP-PRP.The degree and function of knee pain were assessed by visual analog scale(VAS)and knee arthri-tis index scale(WOMAC)at 1,3 and 6 months after injection.Knee MRI was performed after 6 months of treatment,and the MRI imaging changes before and after treatment were compared.Different influencing factors in the treatment results were grouped and analyzed,mainly including platelet concentration in LP-PRP and K-L grading of knee joint.According to the platelet concentration in LP-PRP,it was divided into three grades,which are low concentration[(＜800)×109/L],medium concentration[(800-1 000)×109/L],and high concentration[(＞1 000)× 109/L];According to the K-L grade of the knee joint,the severity of knee osteoarthritis was divided into three grades:Ⅰ、Ⅱ、Ⅲ.Results The VAS and WOMAC scores at 1,3 and 6 months after LP-PRP treatment were significantly lower than those before treatment,and the difference was sta-tistically significant(P＜0.05).There was a statistically significant difference in the therapeutic effect of different levels of platelet concentration,and when the platelet concentration was more than 1 000×109/L,the significant effect was the most obvious(P＜0.05).The therapeutic effect of different levels of platelet concentration was statistically significant(P＜0.05).MRI showed that the articular cartilage signal was significantly improved after treatment.Conclusion Autologous LP-PRP injection into knee cavity for the treatment of KO A has a good short-term clinical effect in relieving knee pain.

Objective:To investigate the risk factors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients in plain-sand areas and loess hilly areas of Gansu province.Methods:A total of 1 599 T2DM patients who participated in chronic disease and risk factors monitoring and basic public health service management were selected by multi-stage stratified random sampling method in the sandy plain areas and loess hilly areas of Gansu province. Questionnaire survey, physical measurement and laboratory tests were performed. Multivariate binary logistic model was used to analyze the influencing factors.Results:The prevalence of DKD was 22.1% (174/787) among T2DM patients in the sandy plain areas and 19.1%(155/812) in the loess hilly area, respectively. Hypertension ( OR=3.022), hyperuricemia ( OR=2.114) and HbA1c≥7%( OR=2.231) were the risk factors for DKD in the plain-sand areas, and the risk of DKD increased with age. In the loess hilly areas, female sex ( OR=0.379) was the protective factor for DKD; while duration of disease≥10 years ( OR=2.476), hyperuricemia ( OR=1.907), HbA1c≥7% ( OR=1.927) were the risk factors for DKD; and the risk of DKD increased with the increase of age, and decreased with the increase of per capita monthly income. Conclusions:The prevalence of DKD and its influencing factors are different between sandy plain areas and loess hilly areas in Gansu province. The prevention and treatment of hypertension should be given more attention in sandy plain areas. In addition, the screening of DKD should be conducted among T2DM patients, particularly for those with old age, hyperuricemia and HbA1c≥7% in both areas of the province.

Incontinence-associated dermatitis is one of the common complications in critically ill patients.This paper reviews the research progress of risk prediction models for incontinence-associated dermatitis in critically ill patients,introduces and compares the characteristics and application effects of different risk prediction models.The purpose is to provide ideas for constructing a localized risk prediction model and provide evidence for medical staff to identify risk factors of incontinence-associated dermatitis at an early stage and take preventive measures.